Category: Causality

Only a few years ago, doctors would advise their patients that elevated blood levels of high-density lipoprotein cholesterol (HDL-C), then termed the “good cholesterol”, were beneficial and would protect them against coronary heart disease. This belief has been called into question, however, as neither genetics nor clinical trials could demonstrate that raising HDL-C levels would protect against cardiovascular disease. Our study, published recently in the IJE, casts further doubt on this “not-so-good-anymore” cholesterol by showing that genetic variants that cause higher HDL-C levels also increase the risk for age-related macular degeneration (AMD).

Our study, recently published in the IJE, shows that youths who initiate cannabis use before the age of 17 years are 60% less likely to pursue higher education than those who never used cannabis. An original aspect of our study, which was based on data from the longitudinal TEMPO cohort in France, is that we were able to take into account youths’ psychological and school difficulties in childhood and adolescence, as well as their parents’ characteristics.

In recent decades, cannabis use has become frequent among youths growing up in Europe, North America and Australasia. The potential health effects of cannabis use include reductions in memory and concentration. Because the brain is thought to develop until the age of 25, adolescent substance use could have lasting negative effects on executive functions, which can in turn result in school difficulties and low educational achievement.

Arguments about causal inference in ‘modern epidemiology’ revolve around the ways in which causes can and should be defined. The potential outcomes approach, a formalized kind of counterfactual reasoning, often aided by directed acyclic graphs (DAGs), can be seen as too rigid and too far removed from many of the complex ‘dirty’ problems of social epidemiology, such as social inequalities and racism. If a potential ‘cause’ cannot be manipulated is it sensible to disregard it, relegating it to the ‘not suitable for epidemiology’ category? The use of properly constructed DAGs may aid causal thinking and help plan relevant analyses – Neil Pearce and Debbie Lawlor provide a simple, but excellent discussion of the use of DAGs in their essay review of Causal Inference in Statistics: A Primer by Judea Pearl and colleagues. However, increasingly, DAGs and analyses are constructed by computer programs, such as DAGitty, now available as an R package ‘dagitty’. Useful as such programmes are, the temptation to use evaluations of DAG-dataset inconsistency to generate purely data-driven, post-hoc modifications to DAGs, raises concern about overfitting and biased inference. Continue reading “Causality in Epidemiology – Themed issue”→